Batch Systems

A Multi-Scale Model Predictive Control Strategy

Multi-scale systems defined on trees can provide local time and scale information about the
behavior of the process in contrast to the usual time-domain model and Fourier transforms. Since the model
predictive control (MPC) framework uses a model of the process to determine the optimal control action, improving
the model by using a multi-scale approach will result in controller actions that can compensate for
phenomena that may occur at different scales. This work develops multi-scale models on trees, describes how these
time-scale models can be used in the MPC framework to represent both the process and the disturbance, and
proposes a new optimization strategy to determine the controller actions such that the optimal inputs, at
the finer scales reflect the inputs at the coarser scales. The performance of this multi-scale MPC strategy is
demonstrated on a continuous process and on a chemical batch reactor.

Batch Reactor Control Using a Multiple Model-based Controller Design

This work presents the development of a model-based controller design called Multiple Model
Predictive Control (MMPC) based on a set of linear, time-varying, state space models to regulate
batch processes according to multiple, pre-specified reference profiles. Sufficient conditions for
stability and boundedness of the dynamic evolution of the forced nonlinear system are provided. The
performance of the MMPC design is demonstrated on a model of a batch reactor that represents the
production of a polymer product.